# r2_is n=60 nsamples=2000000 seed=60 imax=3540; mean C = 1.96702 (2-2/n = 1.96667); walks absorbed before imax: 2000000
# columns: label i  r=E N_i e^{i/n}  SE  relSE  e^{-y^2/2 or -i^2/(2n^3)}  E N_i e^{i/n+i^2/(2n^3)}  heuristic (1-i/n^2)^n e^{i/n}
y=0.25 116 1.001813 1.36e-04 1.36e-04 0.969233 1.033508 0.968668
y=0.50 232 0.921905 1.35e-04 1.46e-04 0.882497 1.044229 0.877904
y=1.00 465 0.619806 1.14e-04 1.84e-04 0.606531 1.022420 0.577943
y=1.50 697 0.295655 7.16e-05 2.42e-04 0.324652 0.910280 0.273985
y=2.00 930 0.093502 3.02e-05 3.23e-04 0.135335 0.692329 0.087920
y=3.00 1394 0.001990 1.18e-06 5.92e-04 0.011109 0.178825 0.002130
x=0.5 30 1.019111 1.34e-04 1.31e-04 0.997919 1.021237 0.997907
x=1.0 60 1.017127 1.35e-04 1.33e-04 0.991701 1.025639 0.991608
x=2.0 120 1.000199 1.36e-04 1.36e-04 0.967216 1.034101 0.966482
x=4.0 240 0.913862 1.35e-04 1.48e-04 0.875173 1.044206 0.869730
x=8.0 480 0.597498 1.12e-04 1.87e-04 0.586646 1.018498 0.556521
x=16.0 960 0.077966 2.62e-05 3.35e-04 0.118442 0.658261 0.073586
# sum_{i<=imax} E N_i = 59.000000 (n-1 = 59);  mean excursion length sum i E N_i/(n-1) = 60.0000 (n = 60; rel dev -7.27e-07)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 1.42e-24) = 2.1513  [upward-biased by MC noise]
